Migrate Databricks Workflows (Lakeflow Jobs) to idiomatic ZenML pipelines. Handles concept mapping (Job->pipeline, Task->step, task values->artifact), notebook refactoring, code translation for all Databricks task types (notebook_task, python_wheel_task, sql_task, dbt_task, condition_task, for_each_task, run_job_task, spark_jar_task), scheduling, retry config, compute mapping, and flags unsupported patterns (file arrival triggers, run_if semantics, shared cluster state, DBFS paths) for human review. Use this skill whenever the user mentions Databricks migration, converting Databricks Jobs or Workflows, porting workflows from Databricks, replacing Databricks orchestration with ZenML, or asks how a Databricks concept maps to ZenML -- even if they don't explicitly say "migrate". Also use when they paste Databricks job JSON or notebook code and ask to make it work with ZenML, or when they describe a workflow using Databricks terminology (task, job, notebook_task, dbutils, task values, job clusters, condition_task
Author ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, DockerSettings for remote execution, custom materializers, metadata logging, secrets management, and custom visualizations. Use this skill whenever asked to write a ZenML pipeline, create ZenML steps, make a pipeline work on Kubernetes/Vertex/SageMaker, add Docker settings, write a materializer, create a custom visualization, handle "works locally but fails on cloud" issues, or configure pipeline YAML files. Even if the user doesn't explicitly mention "pipeline authoring", use this skill when they ask to build an ML workflow, data pipeline, or training pipeline with ZenML.
Migrate Metaflow flows and Outerbounds-flavored Metaflow projects to idiomatic ZenML pipelines. Handles concept mapping (FlowSpec->pipeline, @step->@step, self.* artifacts->explicit returns and inputs), code translation for Parameters, IncludeFile, Config, self.next transitions, branch/join, foreach, scheduling, retry/resource/dependency decorators, and flags unsupported or high-risk patterns (@catch, merge_artifacts, resume and checkpoint semantics, recursion, event triggers, @batch) for human review. Use this skill whenever the user mentions Metaflow migration, converting FlowSpec code, porting flows from Metaflow or Outerbounds, replacing Metaflow orchestration with ZenML, or asks how a Metaflow concept maps to ZenML -- even if they don't explicitly say "migrate". Also use when they paste FlowSpec code or describe workflows using Metaflow terminology (self.next, foreach, current, Parameter, IncludeFile, Config, @catch, @kubernetes, @batch, Runner, Deployer) in a ZenML context. If the user just asks a quick
Migrate Prefect flows, tasks, and deployment patterns to idiomatic ZenML pipelines. Handles concept mapping (`@flow`→`@pipeline`, `@task`→`@step`, result persistence→artifacts), dynamic-execution analysis, code translation, scheduling, retries, Blocks/secrets decomposition, and flags unsupported patterns (`allow_failure()`, `return_state=True`, pause/suspend, global concurrency, task-runner semantics) for human review. Use this skill whenever the user mentions Prefect migration, converting Prefect flows, porting workflows from Prefect, replacing Prefect with ZenML, or asks how a Prefect concept maps to ZenML — even if they do not explicitly say "migrate". Also use when they paste Prefect code and ask to make it work with ZenML, or when they describe a workflow using Prefect terminology (`@flow`, `@task`, `.submit()`, `.map()`, `State`, Blocks, Deployments, work pools, Automations) in a ZenML context. If the user asks a quick conceptual question ("what is the ZenML equivalent of a Prefect Block?"), answer it d
Migrate Flyte workflows, tasks, LaunchPlans, and Flytekit code to idiomatic ZenML pipelines. Handles concept mapping (`@task`->`@step`, `@workflow`->`@pipeline`, `map_task()`->dynamic `.map()`, `conditional()`->dynamic branching, `LaunchPlan`->schedule/config split), code translation, special-type migration (`FlyteFile`, `FlyteDirectory`, `StructuredDataset`, `FlyteSchema`), Docker/image mapping, and flags unsupported patterns (`@eager`, `ContainerTask`, reference entities, checkpointing, interruptible semantics) for human review. Use this skill whenever the user mentions Flyte migration, converting Flyte to ZenML, porting Flyte workflows, replacing Flyte with ZenML, or asks how a Flyte concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste Flytekit code and ask to make it work with ZenML, or when they describe a workflow using Flyte terminology (`@dynamic`, `LaunchPlan`, `map_task`, `conditional`, `ImageSpec`, `FlyteFile`, `StructuredDataset`, `reference_task`, `refer
Migrate Dagster assets, ops, graphs, jobs, and software-defined asset workflows to idiomatic ZenML pipelines. Handles concept mapping (asset->step output, job->pipeline, IOManager->artifact store/materializer + explicit IO steps), asset-boundary planning, code translation, scheduling, retry config, resources/config migration, and flags unsupported patterns (asset selection, partitions/backfills, sensors, declarative automation, freshness policies, observable source assets) for human review. Use this skill whenever the user mentions Dagster migration, converting Dagster assets or jobs, porting workflows from Dagster, replacing Dagster with ZenML, or asks how a Dagster concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste Dagster code and ask to make it work with ZenML, or when they describe a workflow using Dagster terminology (`@asset`, `@multi_asset`, `Definitions`, `IOManager`, `ConfigurableResource`, partitions, sensors, asset checks) in a ZenML context. If the use
Migrate Azure Machine Learning SDK v2 pipelines, components, environments, and schedules to idiomatic ZenML pipelines. Handles concept mapping (`@pipeline` -> `@pipeline`, `@command_component` -> `@step`, `Environment(...)` -> `DockerSettings(...)`, AzureML compute -> `AzureMLOrchestratorSettings`), code translation, Azure-aware "keep AzureML" migration paths, and flags unsupported or unsafe patterns (sweep jobs, parallel jobs, managed endpoints, AzureML Registry, Responsible AI dashboard, and unverified control-flow helpers like `if_else` and `do_while`) for human review. Use this skill whenever the user mentions AzureML migration, Azure Machine Learning SDK v2 migration, converting AzureML pipelines or components, porting workflows from AzureML, replacing AzureML authoring with ZenML, or asks how AzureML concepts map to ZenML -- even if they don't explicitly say "migrate". Also use when they paste AzureML SDK v2 code, `mldesigner` components, YAML components, `load_component()` usage, MLTable/data asset def
Migrate Amazon SageMaker Pipelines and workflow code to idiomatic ZenML pipelines. Handles concept mapping (Pipeline->@pipeline, ProcessingStep/TrainingStep->@step, PropertyFile/JsonGet->artifacts), code translation, SagemakerOrchestratorSettings mapping, scheduling, model-registration strategy, and flags unsupported or high-risk patterns (CallbackStep, LambdaStep handshake semantics, step.properties placeholders, dynamic-pipeline scheduling on SageMaker) for human review. Use this skill whenever the user mentions SageMaker migration, converting SageMaker Pipelines, porting workflow code from SageMaker, replacing SageMaker DSL authoring with ZenML, or asks how a SageMaker Pipelines concept maps to ZenML -- even if they do not explicitly say "migrate". Also use when they paste `sagemaker.workflow.*` code and ask to make it work with ZenML, or when they describe a workflow using SageMaker terms (`ProcessingStep`, `TrainingStep`, `ConditionStep`, `PropertyFile`, `JsonGet`, `ModelStep`, `PipelineSession`) in a Ze